Distributed algorithms for opportunistic spectrum access in decentralized cognitive radio network

The opportunistic spectrum access (OSA) algorithms aim to maximize network throughput by ensuring orthogonal channel allocation among secondary users (SUs) in cognitive radio network (CRN). OSA is challenging in the decentralized CRN due to lack of coordination among SUs. Most of the existing algorithms assume prior knowledge of the number of active SUs to guarantee orthogonalization. In addition, they assume static network only where all SUs stay in the network throughout the horizon which is not feasible in practical conditions. However the dynamic network may also exist where the SUs can enter or exit the network at any time. Also, most of the existing algorithms assume an i.i.d. reward model whereas a markovian model may be more appropriate where the rewards are assumed to come from a finite, irreducible and aperiodic Markov chain represented by a single parameter probability transition matrix. Thus, our goal is to design a distributed algorithm for decentralized static and dynamic network without prior knowledge of the number of active SUs with i.i.d. as well as markovian reward model.